ops = "I"
op += (Op(ops, term1[1] + term2[1] + term3[1] + term4[1]), )
#print(p,q,r,s)
#print(key)
#print(op,(-1)**(npermute%2), npermute)
op += (h2e[p, q, r, s] * (-1)**(npermute % 2), )
model[key].append(op)
return model
start = time.time()
dump_dir = "./"
job_name = "qc" #########
log.register_file_output(dump_dir + job_name + ".log", mode="w")
logger = logging.getLogger(__name__)
spatial_norbs = 7
spin_norbs = spatial_norbs * 2
h1e, h2e, nuc = read_fcidump("h2o_fcidump.out", spatial_norbs)
# a randon integral
#h1e = np.random.uniform(-1,1,size=(spin_norbs,spin_norbs))
#h2e = np.random.uniform(-1,1,size=(spin_norbs,spin_norbs,spin_norbs,spin_norbs))
#h1e = 0.5*(h1e+h1e.T)
#h2e = 0.5*(h2e+h2e.transpose((2,3,0,1)))
model = qc_model(h1e, h2e)
order = {}
import yaml
from renormalizer.model import load_from_dict
from renormalizer.transport import ChargeTransport
from renormalizer.utils import log, Quantity, EvolveConfig, EvolveMethod, RungeKutta, CompressConfig, BondDimDistri
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if len(sys.argv) != 2:
print("No or more than one parameter file are provided, abort")
exit(1)
parameter_path = sys.argv[1]
with open(parameter_path) as fin:
param = yaml.safe_load(fin)
log.register_file_output(
os.path.join(param["output dir"], param["fname"] + ".log"), "w")
mol_list, temperature = load_from_dict(param, 3, False)
compress_config = CompressConfig(threshold=1e-4)
evolve_config = EvolveConfig(adaptive=True, evolve_dt=2)
ct = ChargeTransport(
mol_list,
temperature=temperature,
compress_config=compress_config,
evolve_config=evolve_config,
rdm=False,
)
# ct.stop_at_edge = True
# ct.memory_limit = 2 ** 30 # 1 GB
# ct.memory_limit /= 10 # 100 MB
ct.dump_dir = param["output dir"]
ct.job_name = param["fname"]